政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/143187

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/143187

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题名:	濾嘴法則應用在加密貨幣的獲利性 Profitability of Applying Filter Rule to Cryptocurrency
作者:	羅健捷 Lo, Chien-Chieh
贡献者:	郭維裕 Kuo, Wei-yu 羅健捷 Lo, Chien-Chieh
关键词:	加密貨幣傳統濾嘴法則純做多濾嘴法則使用擴張窗口的濾嘴法則 BTC ETH LTC XRP Cryptocurrency Filter rule Filter rule with expanding window ETH BTC LTC XRP
日期:	2022
上传时间:	2023-02-01 14:05:16 (UTC+8)
摘要:	這幾年，加密貨幣發展興盛，越來越多人將加密貨幣當成一種投資標的，因此除了一開始的去中心化交易平台，現在也越來越多集中交易所，例如：Binance(幣安)、Coinbase、MAX等等，讓更多投資人可以不用開設任何加密貨幣的錢包，直接在平台上註冊一個帳戶，就可以購買加密貨幣，使投資加密貨幣的流程更加簡單方便。因此本研究在文獻探討中，討論很多有關於加密貨幣的各種特質，包括其波動度、與經濟指標的相關性、加密貨幣投資者的投資傾向，再討論已被使用在加密貨幣上的投資策略，並且陳述這些研究發現的結果，最後依據前述研究發現加密貨幣市場有羊群效應以及更適合類似動能交易的策略為方向，從而決定使用傳統濾嘴法則、純做多或做空的濾嘴法則、加上擴張窗口的濾嘴法則，三種不同方式的策略應用在BTC、ETH、LTC、XRP四種貨幣上，討論累積報酬率、交易次數、平均報酬等等，其中不同與以往的濾嘴法則，以前的濾嘴法則單純使用對稱的條件組合，本研究的漲跌幅條件範圍從0.03到0.08，每0.005為一個單位，各有11個條件，從中組合出121種不對稱的漲跌幅條件組合。從研究結果發現，傳統濾嘴法則應用在上述的四種加密貨幣上，在大部分情況下都無法獲得比買進持有策略更好的報酬率，但當該策略變成純做多或純做空的濾嘴法則後，發現純做多的策略，雖然不是在所有條件皆能打代買進持有策略，但明顯報酬率大多數比傳統濾嘴法則來得更好，同時也意味著純做空的濾嘴法則多半處於虧損狀態，最後，為了測試是否使用歷史資料中的最佳濾嘴條件，可以在未來的交易中獲得更好的報酬，本研究結合擴張窗口的方式來執行濾嘴法則，並且每兩個月做一次調整，但結果是歷史資料在該策略中無法獲得較好的報酬率表現。 In recent years, the development of cryptocurrencies has flourished, and more and more people regard cryptocurrencies as a subject of investment. Therefore, in addition to the initial decentralized trading platforms, there are also more and more new exchanges, such as: Binance, Coinbase, MAX, etc., so that more investors can directly buy cryptocurrencies without opening any cryptocurrency wallets, making the process of investing in cryptocurrencies easier and more convenient. In the literature review, many characteristics of cryptocurrencies are discussed, including their volatility, correlation with economic indicators, the investment tendency of cryptocurrency investors, and the investment strategies that have been used in cryptocurrencies, and it states the results of these research findings. According to the characteristics found in the aforementioned research, the cryptocurrency market has a herd effect and is more suitable for strategies similar to momentum trading. It is decided to use traditional filter rule, pure long or short filter rule, and the filter rule with the expansion window. Three different strategies are applied to BTC, ETH, LTC, and XRP to discuss the cumulative return, the number of transactions, the average return, etc. The filter rule is slightly different from the previous filter rule, which are used by Fama and Blume (1966), in filters combination. The previous filter rule simply used a symmetrical combination of filter conditions; however, the range of the rise and fall conditions in this study is from 0.03 to 0.08, each 0.005 is an unit, and each has 11 conditions, therefore there are 121 combination of rise and fall conditions. In the result, it is found that the traditional filter rules applied to the above four cryptocurrencies cannot obtain a better return than the buy and hold strategy in most cases. But when the strategy became pure long or pure short filter rule, It found that the pure long strategy, although it did not win buy and hold strategy under all conditions, obviously most of the returns are better than the traditional filter rule. Meanwhile, it also means that the pure short filter rule is mostly at a loss. Finally, in order to test whether using the best filter conditions in the historical data can get better returns in future trades, this study discuss the filter rule with an expanding window, and adjustment of investment is made every two months, but the result is that historical data does not provide a better return performance in this strategy.
參考文獻:	Almujamed, H. I., 2018, Filter rule performance in an emerging market: evidence from Qatari listed companies. International Journal of Productivity and Performance Management. Arnott, R. D., et al., 2005, Fundamental indexation, Financial Analysts Journal 61(2), 83-99. Baek, C. and M. Elbeck, 2015, Bitcoins as an investment or speculative vehicle? A first look, Applied Economics Letters 22(1), 30-34. Borri, N., 2019, Conditional tail-risk in cryptocurrency markets, Journal of Empirical Finance 50, 1-19. Cooper, M., 1999, Filter rules based on price and volume in individual security overreaction, The Review of Financial Studies 12(4), 901-935. Corbet, S., et al., 2019, The effectiveness of technical trading rules in cryptocurrency markets, Finance Research Letters 31, 32-37. Detzel, Liu, Strauss, Zhou, and Zhu, 2021, Learning and predictability via technical analysis: Evidence from bitcoin and stocks with hard‐to‐value fundamentals, Financial Management 50(1), 107-137. Elgammal, M. M., et al., 2021, The predictive ability of stock market factors, Studies in Economics and Finance. Fama, E. F. and M. E. Blume (1966)., Filter rules and stock-market trading, The Journal of Business 39(1), 226-241. Jennergren, L. P., 1975, A Note on Filter Trading on the Stockholm Stock Exchange, The Swedish Journal of Economics 77(2), 252-259. Kyriazis, N. A., 2019, A survey on efficiency and profitable trading opportunities in cryptocurrency markets, Journal of Risk and Financial Management 12(2), 67. Kyriazis, N. A., 2021, A survey on volatility fluctuations in the decentralized cryptocurrency financial assets, Journal of Risk and Financial Management 14(7), 293. Leirvik, T., 2022, Cryptocurrency returns and the volatility of liquidity, Finance Research Letters 44, 102031. Liu, W., 2019, Portfolio diversification across cryptocurrencies, Finance Research Letters 29, 200-205. Liu, Y. and A. Tsyvinski, 2021, Risks and returns of cryptocurrency, The Review of Financial Studies 34(6), 2689-2727. Liu, Y., et al., 2019, Common risk factors in cryptocurrency, National Bureau of Economic Research. Urquhart, A., 2017, Price clustering in Bitcoin, Economics letters 159, 145-148. 周建新 and 陳振遠, 2021, 濾嘴法則操作績效與台灣期貨市場效率性之研究.
描述:	碩士國立政治大學國際經營與貿易學系 109351034
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0109351034
数据类型:	thesis
显示于类别:	[國際經營與貿易學系 ] 學位論文

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